class_docstr: Implementation of Gaussian likelihood, which is used for regression problems.
  
  Gaussian likelihood uses :class:`~pyro.distributions.Normal` distribution.
  
  :param torch.Tensor variance: A variance parameter, which plays the role of
      ``noise`` in regression problems.
code: def __init__(self, variance=None):
      super().__init__()
  
      variance = torch.tensor(1.0) if variance is None else variance
      self.variance = PyroParam(variance, constraints.positive)
  
file_path: pyro/pyro/contrib/gp/likelihoods/gaussian.py
imports: import torch
  from torch.distributions import constraints
  import pyro
  import pyro.distributions
  from pyro.contrib.gp.likelihoods.likelihood import Likelihood
  from pyro.nn.module import PyroParam
label_desc: Use image node_id 1 to create a new Gaussian object from inherited base classes: Likelihood with example: obj = Gaussian(variance)